Publications [#348803] of Vahid Tarokh

Papers Published

1. Feng, Y; Zhou, Y; Tarokh, V, Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing, Proceedings 2019 Ieee International Conference on Big Data, Big Data 2019 (December, 2019), pp. 2240-2246, ISBN 9781728108582 [doi]
   (last updated on 2023/06/01)

   Abstract:
   We propose an adaptive signal sampling approach that dynamically adjusts the sampling rate to approximate the local Nyquist rate of the signal. The proposed adaptive sampling approach consists of a recurrent neural network-based change detector that detects the point of frequency change and a local Nyquist rate estimator based on a multi-rate signal processing scheme. We empirically demonstrate that our adaptive sampling approach significantly reduces the overall sampling rate for various types of signals and therefore improves the computational efficiency of subsequent signal processing.